Addressing accuracy and precision issues in iTRAQ quantitation
Ontology highlight
ABSTRACT:
INSTRUMENT(S): QSTAR XL, instrument model, QSTAR
ORGANISM(S): Erwinia Carotovora
SUBMITTER: 
Phil Charles
PROVIDER: PRD000105 | Pride | 2010-11-10
REPOSITORIES: pride
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Project description:iTRAQ (isobaric tags for relative or absolute quantitation) is a mass spectrometry technology that allows quantitative comparison of protein abundance by measuring peak intensities of reporter ions released from iTRAQ-tagged peptides by fragmentation during MS/MS. However, current data analysis techniques for iTRAQ struggle to report reliable relative protein abundance estimates and suffer with problems of precision and accuracy. The precision of the data is affected by variance heterogeneity: low signal data have higher relative variability; however, low abundance peptides dominate data sets. Accuracy is compromised as ratios are compressed toward 1, leading to underestimation of the ratio. This study investigated both issues and proposed a methodology that combines the peptide measurements to give a robust protein estimate even when the data for the protein are sparse or at low intensity. Our data indicated that ratio compression arises from contamination during precursor ion selection, which occurs at a consistent proportion within an experiment and thus results in a linear relationship between expected and observed ratios. We proposed that a correction factor can be calculated from spiked proteins at known ratios. Then we demonstrated that variance heterogeneity is present in iTRAQ data sets irrespective of the analytical packages, LC-MS/MS instrumentation, and iTRAQ labeling kit (4-plex or 8-plex) used. We proposed using an additive-multiplicative error model for peak intensities in MS/MS quantitation and demonstrated that a variance-stabilizing normalization is able to address the error structure and stabilize the variance across the entire intensity range. The resulting uniform variance structure simplifies the downstream analysis. Heterogeneity of variance consistent with an additive-multiplicative model has been reported in other MS-based quantitation including fields outside of proteomics; consequently the variance-stabilizing normalization methodology has the potential to increase the capabilities of MS in quantitation across diverse areas of biology and chemistry.
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Project description:Precise and accurate quantification of protein expression levels in a complex biological setting is challenging. Here, we describe a method for absolute quantitation of endogenous proteins in cell lysates using an automated capillary immunoassay system, the size-based Simple Western system (recently developed by ProteinSimple). The method was able to accurately measure the absolute amounts of target proteins at picogram or sub-picogram levels per nanogram of cell lysates. The measurements were independent of the cell matrix or the cell lysis buffer and were not affected by different antibody affinities for their specific epitopes. We then applied this method to quantitate absolute levels of expression of protein kinase C (PKC) isoforms in LNCaP and U937 cells, two cell lines used extensively for probing the downstream biological responses to PKC targeted ligands. Our absolute quantitation confirmed the predominance of PKC? in both cells, supporting the important functional role of this PKC isoform in these cell lines. The method described here provides an approach to accurately quantitate levels of protein expression and correlate protein level with function. In addition to enhanced accuracy relative to conventional Western analysis, it circumvents the distortions inherent in comparison with signal intensities from different antibodies with different affinities.
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2011-06-23 | PRD000444 | Pride
Project description:Multiplexed quantitative mass spectrometry-based proteomics is shaped by numerous opposing propositions. With the emergence of multiplexed single-cell proteomics, studies increasingly present single cell measurements in conjunction with an abundant congruent carrier to improve precursor selection and enhance identifications. While these extreme carrier spikes are often >100-times more abundant than the investigated samples, undoubtedly the total ion current increases but quantitative accuracy possibly is affected. We here focus on narrowly titrated carrier spikes (i.e. <20x) and evaluate the elimination of such for comparable sensitivity at superior accuracy. We find that subtle changes in the carrier ratio can severely impact measurement variability and describe alternative multiplexing strategies to evaluate data quality. Lastly, we demonstrate elevated replicate overlap, while preserving acquisition throughput at improved quantitative accuracy with DIA-TMT and discuss optimized experimental designs for multiplexed proteomics of trace samples. This comprehensive benchmarking gives an overview of currently available techniques and guides through conceptualizing the optimal single-cell proteomics experiment.
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2011-09-02 | PRD000312 | Pride
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2010-02-19 | PRD000815 | Pride
Project description:The use of internal calibrants (the so called lock mass approach) provides much greater accuracy in mass spectrometry based proteomics. However, the polydimethylcyclosiloxane (PCM) peaks commonly used for this purpose are quite unreliable, leading to missing calibrant peaks in spectra and correspondingly lower mass measurement accuracy. Therefore, we here introduce a universally applicable and robust internal calibrant, the tripeptide Asn3. We show that Asn3 is a substantial improvement over PCM both in terms of consistent detection and resulting mass measurement accuracy. Asn3 is also very easy to adopt in the lab, as it requires only minor adjustments to the analytical setup. Data analysis: For mass measurement accuracy (MMA) calculations and comparisons, the following Mascot workflow was used. From the MS/MS data in each LC run, Mascot Generic Files were created using Distiller software (version 2.4.3.3, Matrix Science, London, UK, www.matrixscience.com/distiller.html). These peak lists were then searched with the Mascot search engine (Matrix Science) using the Mascot Daemon interface (version 2.4.0, Matrix Science). Spectra were searched against the Swiss-Prot database (version 13_04 of UniProtKB/Swiss-Prot protein database containing 20,232 sequence entries of human proteins) concatenated with its reversed sequence database. Variable modifications were set to pyro-glutamate formation of amino terminal glutamine and acetylation of the protein N-terminus, whereas fixed modifications only included oxidation of methionine. Mass tolerance on peptide ions was set to 10 ppm (with Mascot’s C13 option set to 1), and the mass tolerance on peptide fragment ions was set to 20 millimass units (mmu), except for the space-charge effect experiment(LMA5) where an extra search was done with a setting of 3 mmu. The peptide charge was set to 1+,2+,3+ and instrument setting was put on ESI-QUAD. Enzyme was set to trypsin allowing for one missed cleavage, and cleavage was allowed when arginine or lysine is followed by proline. Only peptides that were ranked one and scored above the threshold score, set at 99% confidence, were withheld. All data was processed and managed by ms_lims.
2013-10-22 | PXD000426 | Pride
Project description:BACKGROUND: Recent development of novel technologies paved the way for quantitative proteomics. One of the most important among them is iTRAQ, employing isobaric tags for relative or absolute quantitation. Despite large progress in technology development, still many challenges remain for derivation and interpretation of quantitative results. One of these challenges is the consistent assignment of peptides to proteins. RESULTS: We have developed Peptide Profiling Guided Identification of Proteins (PPINGUIN), a statistical analysis workflow for iTRAQ data addressing the problem of ambiguous peptide quantitations. Motivated by the assumption that peptides uniquely derived from the same protein are correlated, our method employs clustering as a very early step in data processing prior to protein inference. Our method increases experimental reproducibility and decreases variability of quantitations of peptides assigned to the same protein. Giving further support to our method, application to a type 2 diabetes dataset identifies a list of protein candidates that is in very good agreement with previously performed transcriptomics meta analysis. Making use of quantitative properties of signal patterns identified, PPINGUIN can reveal new isoform candidates. CONCLUSIONS: Regarding the increasing importance of quantitative proteomics we think that this method will be useful in practical applications like model fitting or functional enrichment analysis. We recommend to use this method if quantitation is a major objective of research.
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